初始化项目，由ModelHub XC社区提供模型

Model: malhajar/Mistral-7B-Instruct-v0.2-turkish
Source: Original Platform

README.md

@@ -0,0 +1,57 @@
+---
+license: apache-2.0
+datasets:
+- malhajar/alpaca-gpt4-tr
+language:
+- tr
+- en
+---
+
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+malhajar/Mistral-7B-Instruct-v0.2-turkish is a finetuned version of [`Mistral-7B-Instruct-v0.2`]( https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) using SFT Training and Freeze method.
+This model can answer information in a chat format as it is finetuned specifically on instructions specifically [`alpaca-gpt4-tr`]( https://huggingface.co/datasets/malhajar/alpaca-gpt4-tr) 
+
+### Model Description
+
+- **Developed by:** [`Mohamad Alhajar`](https://www.linkedin.com/in/muhammet-alhajar/) 
+- **Language(s) (NLP):** Turkish
+- **Finetuned from model:** [`mistralai/Mistral-7B-Instruct-v0.2`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+
+### Prompt Template
+```
+### Instruction:
+
+<prompt> (without the <>)
+
+### Response:
+```
+
+## How to Get Started with the Model
+
+Use the code sample provided in the original post to interact with the model.
+```python
+from transformers import AutoTokenizer,AutoModelForCausalLM
+ 
+model_id = "malhajar/Mistral-7B-Instruct-v0.2-turkish"
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
+                                             device_map="auto",
+                                             torch_dtype=torch.float16,
+                                             revision="main")
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+question: "Türkiyenin en büyük şehir nedir?"
+# For generating a response
+prompt = '''
+### Instruction:  {question} ### Response:
+'''
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+output = model.generate(inputs=input_ids,max_new_tokens=512,pad_token_id=tokenizer.eos_token_id,top_k=50, do_sample=True,repetition_penalty=1.3
+        top_p=0.95,trust_remote_code=True,)
+response = tokenizer.decode(output[0])
+
+print(response)
+```